University of New Mexico. Dept. of Electrical and Computer Engineering

LC Subject(s)

Cognitive radio networks.Radio frequency allocation.

Degree Level

Masters

Abstract

We propose to use binary consensus algorithms for distributed cooperative spectrum sensing in cognitive radio networks. We propose to use two binary approaches, namely diversity and fusion binary consensus spectrum sensing. The performance of these algorithms is analyzed over fading channels. The probability of networked detection and false alarm are characterized for the diversity case. We then compare the performance of our binary-based cooperative spectrum sensing framework to that of the already-existing averaged-based one. We show that binary consensus cooperative spectrum sensing is superior to quantized average consensus in terms of agility, given the same number of transmitted bits. We furthermore derive a lower bound for the performance of the average consensus-based spectrum sensing.
We then extend our diversity-based framework to propose a weighted approach in which each secondary user utilizes a set of weights to account for different local
sensing qualities of its neighbors as well as different communication link qualities from them. We mathematically characterize the optimum weights. Finally, the impact of network configuration (in terms of average distance between the secondary users) and the resulting correlated measurements (due to shadow fading)
are considered on the overall networked detection performance. More specifically, we consider the impact of the average distance on both the correlation of the
sensing measurements of the secondary users and the connectivity of the underlying
graph among them. We discuss interesting underlying tradeoffs when increasing or decreasing the average distance.